1. Technological Field
This disclosure pertains generally to image inpainting, and more particularly to utilizing depth information for recovering missing information when performing image frame inpainting. Herein, depth information refers to the distances of points in the scene from the camera that captured the image, or their distances from such camera when measured along the optical axis of the camera.
2. Background Discussion
The process of image inpainting involves reconstructing missing parts of an image frame, or video frame, that have been destroyed or intentionally removed. If an object captured within an image is deleted, or moved, the area that was covered by (obscured by) that object in the original image, has to be reconstructed so that the resulting image still appears “natural” looking. That is to say that it is desirable that if the viewer has not seen the original image, they would be unable to notice that the image has been altered by removing, or moving an object.
There are a number of current inpainting techniques described in the literature. In general, these methods can be categorized into the following categories: (a) diffusion-based approaches, (b) sparse representation of images, and (c) exemplar-based approaches.
These methods can produce generally suitable outputs when the inpainting process involves homogeneous and/or small areas with no complicated structures. However, in scenes involving large missing areas with multiple occluded regions and structures, these techniques leave visible artifacts in the resultant image, especially in the edge and structured areas.
Accordingly, a need exists for inpainting reconstruction techniques which are able to generate desirable results even for large inpainted areas which include structure.